Data quality may relate to the accuracy of data and whether the proper data is being captured to meet the user's needs and whether the data is available when it is needed. Data quality may be important for a variety of reasons. For example, in a chemical or pharmaceutical manufacturing scenario, data quality may be important to accurately determine the amount of active ingredients in raw stock which impacts the chemical or pharmaceutical manufactured from the raw stock. In another scenario, inventory data quality may be important to ensure the proper amount of raw stock is delivered and available to produce a certain amount of chemical. In another scenario, data quality may be important for ensuring regulatory compliance. Sarbanes-Oxley and other government regulations may require an organization to maintain strict records and provide accurate reports. Failure to comply may result in harsh penalties. In another example, data quality may be important for making accurate predictions. For example, predictions regarding the weather or predictions regarding the stock market may be impacted by the quality of the data used to make the predictions. In some cases, data must be available in a timely manner and the level of data integrity must be high to perform daily operations and to ensure reactions and decisions based on the data are justified.
Given the large amounts of data that may be generated, and in some cases the requirements for accessing the data in a short time frame, it is often difficult to measure and monitor data accuracy. Furthermore, even if data accuracy were to be monitored, it is often difficult to implement fixes for inaccurate data in a timely manner.